In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.
suppressPackageStartupMessages({
library(tibble)
library(rcrossref)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(gh)
library(purrr)
library(jsonlite)
library(DT)
library(plotly)
library(citecorp)
library(readr)
library(rworldmap)
library(gt)
library(stringr)
library(openalexR)
})## Keep track of the source of each column
source_track <- c()
## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
dcap <- lubridate::today()
} else {
dcap <- ""
}## Get list of countries and populations (2022) from the rworldmap/gt packages
data("countrySynonyms")
country_names <- countrySynonyms |>
select(-ID) |>
pivot_longer(names_to = "tmp", values_to = "name", -ISO3) |>
filter(name != "") |>
select(-tmp)
## Country population data from the World Bank (https://data.worldbank.org/indicator/SP.POP.TOTL),
## distributed via the gt R package
country_populations <- countrypops |>
filter(year == 2022)## Read archived version of summary data frame, to use for filling in
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)
## Similarly for citation analysis, to avoid having to pull down the
## same information multiple times
citations_archive <- readr::read_delim(
url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
col_types = cols(.default = "c"), col_names = TRUE,
delim = "\t")We get the information about published JOSS papers from Crossref,
using the rcrossref R package. The openalexR R
package is used to extract citation counts from OpenAlex.
## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim)$data
i <- 1
while (nrow(papers) == i * lim) {
papers <- dplyr::bind_rows(
papers,
rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim, offset = i * lim)$data)
i <- i + 1
}
papers <- papers %>%
dplyr::filter(type == "journal-article")
dim(papers)## [1] 2546 28
## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
papers$url[noaltid])
## Get citation info from Crossref and merge with paper details
# cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
# papers <- papers %>% dplyr::left_join(
# cit %>% dplyr::rename(citation_count = count),
# by = c("alternative.id" = "doi")
# )
## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")
dim(papers)## [1] 2545 28
## Get info from openalexR and merge with paper details
## Helper function to extract countries from affiliations. Note that this
## information is not available for all papers.
.get_countries <- function(df, wh = "first") {
if (length(df) == 1 && is.na(df)) {
""
} else {
if (wh == "first") {
## Only first affiliation for each author
tmp <- df |>
dplyr::filter(!duplicated(au_id) & !is.na(institution_country_code)) |>
pull(institution_country_code)
} else {
## All affiliations
tmp <- df |>
dplyr::filter(!is.na(institution_country_code)) |>
pull(institution_country_code)
}
if (length(tmp) > 0) {
tmp |>
unique() |>
paste(collapse = ";")
} else {
""
}
}
}
oa <- oa_fetch(entity = "works",
primary_location.source.id = "s4210214273") |>
mutate(affil_countries_all = vapply(author, .get_countries, "", wh = "all"),
affil_countries_first = vapply(author, .get_countries, "", wh = "first"))## Warning in oa_request(oa_query(filter = filter_i, multiple_id = multiple_id, :
## The following work(s) have truncated lists of authors: W3005984879.
## Query each work separately by its identifier to get full list of authors.
## For example:
## lapply(c("W3005984879"), \(x) oa_fetch(identifier = x))
## Details at https://docs.openalex.org/api-entities/authors/limitations.
papers <- papers %>% dplyr::left_join(
oa %>% dplyr::mutate(alternative.id = sub("https://doi.org/", "", doi)) %>%
dplyr::select(alternative.id, cited_by_count, id,
affil_countries_all, affil_countries_first) %>%
dplyr::rename(citation_count = cited_by_count,
openalex_id = id),
by = "alternative.id"
)
source_track <- c(source_track,
structure(rep("OpenAlex", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.
whedon <- list()
p <- 1
a0 <- NULL
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0 && !identical(a, a0)) {
whedon <- c(whedon, a)
p <- p + 1
a0 <- a
a <- tryCatch({
jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)},
error = function(e) return(numeric(0))
)
}
whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
data.frame(api_title = w$title,
api_state = w$state,
editor = paste(w$editor, collapse = ","),
reviewers = paste(w$reviewers, collapse = ","),
nbr_reviewers = length(w$reviewers),
repo_url = w$software_repository,
review_issue_id = sub("https://github.com/openjournals/joss-reviews/issues/",
"", w$paper_review),
doi = w$doi,
prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
w$meta_review_issue_id, NA_integer_),
languages = gsub(", ", ",", w$languages),
archive_doi = w$software_archive)
}))
papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))
dim(papers)## [1] 2545 42
From each pre-review and review issue, we extract information about review times and assigned labels.
## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues",
.limit = 15000, state = "all")## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
data.frame(title = i$title,
number = i$number,
state = i$state,
opened = i$created_at,
closed = ifelse(!is.null(i$closed_at),
i$closed_at, NA_character_),
ncomments = i$comments,
labels = paste(setdiff(
vapply(i$labels, getElement,
name = "name", character(1L)),
c("review", "pre-review", "query-scope", "paused")),
collapse = ","))
}))
## Split into REVIEW, PRE-REVIEW, and other issues (the latter category
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) &
!grepl("\\[REVIEW\\]", title))
dim(issother)## [1] 153 7
## title
## 1 Add a synthetic dataset
## 2 # Post-Review Checklist for Editor and Authors
## 3 Nanonis version incompatibility - Deprecated Slots
## 4 Questions about "statement of need" and the relative contribution of the three authors
## 5 how to include error in the gala dynamics calculation
## 6 Thanks @cudmore for taking the time to review this. Your valuable comments and suggestions greatly improved the quality of the documentation.
## number state opened closed ncomments labels
## 1 6952 closed 2024-07-02T20:56:20Z 2024-07-02T20:56:22Z 1
## 2 6924 closed 2024-06-24T10:12:54Z 2024-06-24T10:12:57Z 1
## 3 6709 closed 2024-05-01T06:48:44Z 2024-05-01T06:48:46Z 1
## 4 6360 closed 2024-02-16T09:50:43Z 2024-02-16T09:50:45Z 1
## 5 6337 closed 2024-02-08T14:36:25Z 2024-02-08T14:36:27Z 1
## 6 6262 closed 2024-01-23T02:39:54Z 2024-01-23T02:39:56Z 1
## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
dplyr::mutate(alternative.id = paste0("10.21105/joss.",
nbrzeros,
number)) %>%
dplyr::select(-nbrzeros) %>%
dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))## For pre-review and review issues, respectively, get the number of
## issues closed each month, and the number of those that have the
## 'rejected' label
review_rejected <- iss %>%
dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "review")
prereview_rejected <- iss %>%
dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "pre-review")
all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)## For PRE-REVIEW issues, add information about the corresponding REVIEW
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!grepl("withdrawn", labels)) %>%
dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>%
dplyr::filter(!duplicated(title)) %>%
dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
dplyr::rename_all(~ paste0("prerev_", .))
papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>%
dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
dplyr::mutate(prerev_opened = as.Date(prerev_opened),
prerev_closed = as.Date(prerev_closed),
review_opened = as.Date(review_opened),
review_closed = as.Date(review_closed)) %>%
dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
days_in_rev = review_closed - review_opened,
to_review = !is.na(review_opened))
dim(papers)## [1] 2545 58
## Reorder so that software repositories that were interrogated longest
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)## [1] 2545
## [1] 2403
## [1] "https://gitlab.com/utopia-project/utopia"
## [2] "https://gitlab.inria.fr/bramas/tbfmm"
## [3] "https://gitlab.com/myqueue/myqueue"
## [4] "https://bitbucket.org/orionmhdteam/orion2_release1/src/master/"
## [5] "https://gitlab.com/fduchate/predihood"
## [6] "https://git.ligo.org/asimov/asimov"
## [7] "https://jugit.fz-juelich.de/compflu/swalbe.jl/"
## [8] "https://gitlab.com/moerman1/fhi-cc4s"
## [9] "https://gitlab.com/ENKI-portal/ThermoCodegen"
## [10] "https://gitlab.com/wpettersson/kep_solver"
## [11] "https://gitlab.com/jtagusari/hrisk-noisemodelling"
## [12] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
## [13] "https://bitbucket.org/meg/cbcbeat"
## [14] "https://gitlab.pasteur.fr/vlegrand/ROCK"
## [15] "https://gitlab.mpikg.mpg.de/curcuraci/bmiptools"
## [16] "https://gitlab.com/pyFBS/pyFBS"
## [17] "https://gitlab.dune-project.org/dorie/dorie"
## [18] "https://gitlab.kuleuven.be/ITSCreaLab/public-toolboxes/dyntapy"
## [19] "https://gitlab.com/dmt-development/dmt-core"
## [20] "https://gitlab.com/dlr-ve/esy/remix/framework"
## [21] "https://gitlab.com/cosmograil/starred"
## [22] "https://gitlab.com/ffaucher/hawen"
## [23] "https://savannah.nongnu.org/projects/complot/"
## [24] "https://gitlab.inria.fr/miet/miet"
## [25] "https://gitlab.com/jason-rumengan/pyarma"
## [26] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [27] "http://mutabit.com/repos.fossil/grafoscopio/"
## [28] "https://gitlab.com/bonsamurais/bonsai/util/ipcc"
## [29] "https://gitlab.com/cerfacs/batman"
## [30] "https://bitbucket.org/manuela_s/hcp/"
## [31] "https://bitbucket.org/hammurabicode/hamx"
## [32] "https://gitlab.com/petsc/petsc"
## [33] "https://gitlab.inria.fr/bcoye/game-engine-scheduling-simulation"
## [34] "https://gitlab.com/fibreglass/pivc"
## [35] "https://gitlab.com/culturalcartography/text2map"
## [36] "https://codebase.helmholtz.cloud/mussel/netlogo-northsea-species.git"
## [37] "https://gitlab.com/gdetor/genetic_alg"
## [38] "https://bitbucket.org/berkeleylab/hardware-control/src/main/"
## [39] "https://gitlab.com/utopia-project/dantro"
## [40] "https://gitlab.com/akantu/akantu"
## [41] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [42] "https://gite.lirmm.fr/doccy/RedOak"
## [43] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"
## [44] "https://gitlab.com/ProjectRHEA/flowsolverrhea"
## [45] "https://gitlab.com/emd-dev/emd"
## [46] "https://gitlab.com/libreumg/dataquier.git"
## [47] "https://git.rwth-aachen.de/ants/sensorlab/imea"
## [48] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [49] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"
## [50] "https://gitlab.com/vibes-developers/vibes"
## [51] "https://gitlab.com/vibes-developers/vibes"
## [52] "https://gitlab.com/dlr-dw/ontocode"
## [53] "https://gitlab.com/sails-dev/sails"
## [54] "https://gitlab.com/marinvaders/marinvaders"
## [55] "https://gitlab.com/marinvaders/marinvaders"
## [56] "https://gitlab.com/mantik-ai/mantik"
## [57] "https://gitlab.com/mantik-ai/mantik"
## [58] "https://gitlab.com/jesseds/apav"
## [59] "https://gitlab.com/jesseds/apav"
## [60] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"
## [61] "https://plmlab.math.cnrs.fr/lmrs/statistique/smmR"
## [62] "https://gitlab.com/InspectorCell/inspectorcell"
## [63] "https://bitbucket.org/bmskinner/nuclear_morphology"
## [64] "https://bitbucket.org/sbarbot/motorcycle/src/master/"
## [65] "https://gitlab.com/binary_c/binary_c-python/"
## [66] "https://gitlab.inria.fr/melissa/melissa"
## [67] "https://gitlab.com/sissopp_developers/sissopp"
## [68] "https://framagit.org/GustaveCoste/off-product-environmental-impact/"
## [69] "https://gitlab.com/tum-ciip/elsa"
## [70] "https://gitlab.com/picos-api/picos"
## [71] "https://gitlab.uliege.be/smart_grids/public/gboml"
## [72] "https://gitlab.com/pvst/asi"
## [73] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [74] "https://www.idpoisson.fr/fullswof/"
## [75] "https://gitlab.kitware.com/LBM/lattice-boltzmann-solver"
## [76] "https://gitlab.com/eidheim/Simple-Web-Server"
## [77] "https://bitbucket.org/glotzer/rowan"
## [78] "https://gitlab.com/cracklet/cracklet.git"
## [79] "https://gitlab.com/toposens/public/ros-packages"
## [80] "https://bitbucket.org/cdegroot/wediff"
## [81] "https://bitbucket.org/basicsums/basicsums"
## [82] "https://gitlab.inria.fr/azais/treex"
## [83] "https://gitlab.com/bioeconomy/forobs/biotrade/"
## [84] "https://gitlab.com/soleil-data-treatment/soleil-software-projects/remote-desktop"
## [85] "https://git.geomar.de/digital-earth/dasf/dasf-messaging-python"
## [86] "https://gitlab.com/sigcorr/sigcorr"
## [87] "https://gitlab.com/dsbowen/conditional-inference"
## [88] "https://gitlab.com/thartwig/asloth"
## [89] "https://code.usgs.gov/umesc/quant-ecology/fishstan/"
## [90] "https://gitlab.com/QComms/cqptoolkit"
## [91] "https://bitbucket.org/sciencecapsule/sciencecapsule"
## [92] "https://framagit.org/GustaveCoste/eldam"
## [93] "https://gitlab.com/fame-framework/fame-core"
## [94] "https://gitlab.com/fame-framework/fame-io"
## [95] "https://gitlab.ifremer.fr/resourcecode/resourcecode"
## [96] "https://gitlab.com/chaver/choco-mining"
## [97] "https://gitlab.com/drti/basic-tools"
## [98] "https://gitlab.com/ags-data-format-wg/ags-python-library"
## [99] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"
## [100] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [101] "https://bitbucket.org/ocellarisproject/ocellaris"
## [102] "https://gitlab.inria.fr/mosaic/bvpy"
## [103] "https://gitlab.com/cosmograil/PyCS3"
## [104] "https://bitbucket.org/berkeleylab/esdr-pygdh/"
## [105] "https://gitlab.com/habermann_lab/phasik"
## [106] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [107] "https://gitlab.com/materials-modeling/wulffpack"
## [108] "https://gitlab.com/dlr-ve/autumn/"
## [109] "https://gitlab.com/moorepants/skijumpdesign"
## [110] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [111] "https://gitlab.com/davidtourigny/dynamic-fba"
## [112] "https://gitlab.com/cmbm-ethz/pourbaix-diagrams"
## [113] "https://bitbucket.org/likask/mofem-cephas"
## [114] "https://git.iws.uni-stuttgart.de/tools/frackit"
## [115] "https://bitbucket.org/cmutel/brightway2"
## [116] "https://gitlab.eudat.eu/coccon-kit/proffastpylot"
## [117] "https://gitlab.com/costrouc/pysrim"
## [118] "https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy"
## [119] "https://gitlab.com/tesch1/cppduals"
## [120] "https://gitlab.com/geekysquirrel/bigx"
## [121] "https://bitbucket.org/cloopsy/android/"
## [122] "https://gitlab.com/celliern/scikit-fdiff/"
## [123] "https://gitlab.com/tue-umphy/software/parmesan"
## [124] "https://bitbucket.org/dghoshal/frieda"
## [125] "https://gitlab.com/gims-developers/gims"
## [126] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [127] "https://gitlab.com/permafrostnet/teaspoon"
## [128] "https://c4science.ch/source/tamaas/"
## [129] "https://gitlab.com/programgreg/tagginglatencyestimator"
## [130] "https://git.mpib-berlin.mpg.de/castellum/castellum"
## [131] "https://gitlab.com/dglaeser/fieldcompare"
## [132] "https://gitlab.com/dlr-ve/esy/sfctools/framework/"
## [133] "https://gitlab.com/robizzard/libcdict"
## [134] "https://gitlab.awi.de/sicopolis/sicopolis"
## [135] "https://gitlab.com/datafold-dev/datafold/"
## [136] "https://gitlab.com/materials-modeling/calorine"
## [137] "https://gitlab.com/energyincities/besos/"
## [138] "https://gitlab.com/mauricemolli/petitRADTRANS"
## [139] "https://bitbucket.org/robmoss/particle-filter-for-python/"
## [140] "https://gitlab.com/pythia-uq/pythia"
## [141] "https://bitbucket.org/mituq/muq2.git"
## [142] "https://gitlab.com/ampere2/metalwalls"
df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
if (grepl("/tree/", u0)) {
u0 <- strsplit(u0, "/tree/")[[1]][1]
}
if (grepl("/blob/", u0)) {
u0 <- strsplit(u0, "/blob/")[[1]][1]
}
info <- try({
gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
})
languages <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"),
.limit = 500)
})
topics <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"),
.accept = "application/vnd.github.mercy-preview+json", .limit = 500)
})
contribs <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"),
.limit = 500)
})
if (!is(info, "try-error") && length(info) > 1) {
if (!is(contribs, "try-error")) {
if (length(contribs) == 0) {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
} else {
repo_nbr_contribs <- length(contribs)
repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
if (is.na(repo_nbr_contribs_2ormore)) {
print(contribs)
}
}
} else {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
}
if (!is(languages, "try-error")) {
if (length(languages) == 0) {
repolang <- ""
} else {
repolang <- paste(paste(names(unlist(languages)),
unlist(languages), sep = ":"), collapse = ",")
}
} else {
repolang <- ""
}
if (!is(topics, "try-error")) {
if (length(topics$names) == 0) {
repotopics <- ""
} else {
repotopics <- paste(unlist(topics$names), collapse = ",")
}
} else {
repotopics <- ""
}
data.frame(repo_url = u,
repo_created = info$created_at,
repo_updated = info$updated_at,
repo_pushed = info$pushed_at,
repo_nbr_stars = info$stargazers_count,
repo_language = ifelse(!is.null(info$language),
info$language, NA_character_),
repo_languages_bytes = repolang,
repo_topics = repotopics,
repo_license = ifelse(!is.null(info$license),
info$license$key, NA_character_),
repo_nbr_contribs = repo_nbr_contribs,
repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
)
} else {
NULL
}
})) %>%
dplyr::mutate(repo_created = as.Date(repo_created),
repo_updated = as.Date(repo_updated),
repo_pushed = as.Date(repo_pushed)) %>%
dplyr::distinct() %>%
dplyr::mutate(repo_info_obtained = lubridate::today())
if (length(unique(df$repo_url)) != length(df$repo_url)) {
print(length(unique(df$repo_url)))
print(length(df$repo_url))
print(df$repo_url[duplicated(df$repo_url)])
}
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)
## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)
papers <- papers %>% dplyr::left_join(df, by = "repo_url")
dim(papers)
source_track <- c(source_track,
structure(rep("sw-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
dplyr::mutate(published.date = as.Date(published.print)) %>%
dplyr::mutate(
halfyear = paste0(year(published.date),
ifelse(month(published.date) <= 6, "H1", "H2"))
) %>% dplyr::mutate(
halfyear = factor(halfyear,
levels = paste0(rep(sort(unique(year(published.date))),
each = 2), c("H1", "H2")))
) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
dim(papers)## [1] 2550 69
## [1] 2490 69
In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.
ggplot(papers %>%
dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
dplyr::group_by(pubmonth) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubmonth), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per month", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))ggplot(papers %>%
dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
dplyr::group_by(pubyear) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubyear), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per year", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.
ggplot(all_rejected,
aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) +
geom_bar(stat = "identity") +
theme_minimal() +
facet_wrap(~ itype, ncol = 1) +
labs(x = "Month of issue closing", y = "Fraction of issues rejected",
caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))Papers with 20 or more citations are grouped in the “>=20” category.
ggplot(papers %>%
dplyr::mutate(citation_count = replace(citation_count,
citation_count >= 20, ">=20")) %>%
dplyr::mutate(citation_count = factor(citation_count,
levels = c(0:20, ">=20"))) %>%
dplyr::group_by(citation_count) %>%
dplyr::tally(),
aes(x = citation_count, y = n)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(x = "OpenAlex citation count", y = "Number of publications", caption = dcap)The table below sorts the JOSS papers in decreasing order by the number of citations in OpenAlex.
DT::datatable(
papers %>%
dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>",
url,"</a>")) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::select(title, url, published.date, citation_count),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)plotly::ggplotly(
ggplot(papers, aes(x = published.date, y = citation_count, label = title)) +
geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() +
geom_smooth() +
labs(x = "Date of publication", y = "OpenAlex citation count", caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: The following aesthetics were dropped during statistical transformation: label.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.
ggplot(papers %>% dplyr::group_by(halfyear) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::mutate(idx = seq_along(citation_count)),
aes(x = idx, y = citation_count)) +
geom_point(alpha = 0.5) +
facet_wrap(~ halfyear, scales = "free") +
theme_bw() +
labs(x = "Index", y = "OpenAlex citation count", caption = dcap)## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
In these plots we investigate whether the time a submission spends in the pre-review or review stage (or their sum) has changed over time. The blue curve corresponds to a rolling median for submissions over 120 days.
## Helper functions (modified from https://stackoverflow.com/questions/65147186/geom-smooth-with-median-instead-of-mean)
rolling_median <- function(formula, data, xwindow = 120, ...) {
## Get order of x-values and sort x/y
ordr <- order(data$x)
x <- data$x[ordr]
y <- data$y[ordr]
## Initialize vector for smoothed y-values
ys <- rep(NA, length(x))
## Calculate median y-value for each unique x-value
for (xs in setdiff(unique(x), NA)) {
## Get x-values in the window, and calculate median of corresponding y
j <- ((xs - xwindow/2) < x) & (x < (xs + xwindow/2))
ys[x == xs] <- median(y[j], na.rm = TRUE)
}
y <- ys
structure(list(x = x, y = y, f = approxfun(x, y)), class = "rollmed")
}
predict.rollmed <- function(mod, newdata, ...) {
setNames(mod$f(newdata$x), newdata$x)
}ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of review opening", y = "Number of days in review",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = prerev_opened,
y = as.numeric(days_in_pre) + as.numeric(days_in_rev))) +
geom_point() +
geom_smooth(formula = y ~ x, method = "rolling_median",
se = FALSE, method.args = list(xwindow = 120)) +
theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review + review",
caption = dcap) +
theme(axis.title = element_text(size = 15))Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.
## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
data.frame(language = l,
nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>%
setNames(c("language", "bytes")) %>%
dplyr::mutate(bytes = as.numeric(bytes)) %>%
dplyr::filter(!is.na(language)) %>%
dplyr::group_by(language) %>%
dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
nbr_repos_GitHub = length(bytes)) %>%
dplyr::arrange(desc(nbr_bytes_GitHub))
langs <- dplyr::full_join(langs, langbytes, by = "language")ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
dplyr::filter(nbr_submissions_Whedon > 10) %>%
dplyr::mutate(language = factor(language, levels = language)),
aes(x = language, y = nbr_submissions_Whedon)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15))DT::datatable(
langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) +
geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() +
theme_bw() +
labs(x = "Number of repos using the language",
y = "Total number of bytes of code\nwritten in the language",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplotly(
ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
label = title)) +
geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() +
theme_bw() +
labs(x = "OpenAlex citation count", y = "Number of stars, GitHub repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from repo creation to JOSS pre-review start",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)) +
facet_wrap(~ year(published.date), scales = "free_y")Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
ggplot(papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)),
aes(x = nbr_reviewers)) + geom_bar() +
facet_wrap(~ year) + theme_bw() +
labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(papers %>%
dplyr::mutate(year = year(published.date),
`r/pyOpenSci` = factor(
grepl("rOpenSci|pyOpenSci", prerev_labels),
levels = c("TRUE", "FALSE"))),
aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) +
theme_bw() + facet_wrap(~ year, ncol = 1) +
scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Editor", y = "Number of submissions", caption = dcap)all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
grep("bsd", all_licenses, value = TRUE),
grep("mit", all_licenses, value = TRUE),
grep("gpl", all_licenses, value = TRUE),
grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>%
dplyr::mutate(repo_license = factor(repo_license,
levels = license_levels)),
aes(x = repo_license)) +
geom_bar() +
theme_bw() +
labs(x = "Software license", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~ year(published.date), scales = "free_y")## For plots below, replace licenses present in less
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::count() %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = n, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Number of submissions", caption = dcap)ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)
colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE,
colors = colors, scale = c(10, 0.1), random.color = TRUE,
ordered.colors = FALSE, vfont = c("serif", "plain")
)Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.
## Split into several queries
## Randomize the splitting since a whole query may fail if one ID is not recognized
papidx <- seq_len(nrow(papers))
idxL <- split(sample(papidx, length(papidx), replace = FALSE), ceiling(papidx / 50))
citationsL <- lapply(idxL, function(idx) {
tryCatch({
citecorp::oc_coci_cites(doi = papers$alternative.id[idx]) %>%
dplyr::distinct() %>%
dplyr::mutate(citation_info_obtained = as.character(lubridate::today()))
}, error = function(e) {
NULL
})
})
citationsL <- citationsL[vapply(citationsL, function(df) !is.null(df) && nrow(df) > 0, FALSE)]
if (length(citationsL) > 0) {
citations <- do.call(dplyr::bind_rows, citationsL)
} else {
citations <- NULL
}
dim(citations)## NULL
if (!is.null(citations) && is.data.frame(citations) && "oci" %in% colnames(citations)) {
citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci))
tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
contains("type"), contains("publisher"), contains("prefix"))
citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume
## that they can be
## identified from the prefix 10.1101 - set the container.title
## for these records manually; we may or may not want to count these
## (would it count citations twice, both preprint and publication?)
citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
## JOSS is represented by 'The Journal of Open Source Software' as well as
## 'Journal of Open Source Software'
citations$container.title[citations$container.title ==
"Journal of Open Source Software"] <-
"The Journal of Open Source Software"
## Remove real self citations (cited DOI = citing DOI)
citations <- citations %>% dplyr::filter(cited != citing)
## Merge with the archive
citations <- dplyr::bind_rows(citations, citations_archive)
} else {
citations <- citations_archive
if (is.null(citations[["citation_info_obtained"]])) {
citations$citation_info_obtained <- NA_character_
}
}
citations$citation_info_obtained[is.na(citations$citation_info_obtained)] <-
"2021-08-11"
write.table(citations, file = "joss_submission_citations.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)## [1] "2024-07-11"
## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 1497
## Number of JOSS papers with >0 citations according to OpenAlex
length(which(papers$citation_count > 0))## [1] 1806
## Number of citations from Open Citations Corpus vs OpenAlex
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## [1] 64752
## [1] 72024
## Ratio of total citation count Open Citations Corpus/OpenAlex
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 1.112305
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "OpenAlex citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))## [1] 8229
## [1] 6150
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)The tibble object with all data collected above is serialized to a file that can be downloaded and reused.
## alternative.id container.title created deposited
## 1 10.21105/joss.03598 Journal of Open Source Software 2021-09-30 2021-09-30
## 2 10.21105/joss.02752 Journal of Open Source Software 2021-10-05 2021-10-05
## 3 10.21105/joss.03917 Journal of Open Source Software 2021-12-02 2021-12-02
## 4 10.21105/joss.00656 Journal of Open Source Software 2018-06-21 2018-06-21
## 5 10.21105/joss.02727 Journal of Open Source Software 2020-12-12 2020-12-12
## 6 10.21105/joss.00850 Journal of Open Source Software 2018-08-20 2018-08-20
## published.print doi indexed issn issue issued
## 1 2021-09-30 10.21105/joss.03598 2023-11-29 2475-9066 65 2021-09-30
## 2 2021-10-05 10.21105/joss.02752 2023-11-28 2475-9066 66 2021-10-05
## 3 2021-12-02 10.21105/joss.03917 2022-03-30 2475-9066 68 2021-12-02
## 4 2018-06-21 10.21105/joss.00656 2024-06-10 2475-9066 26 2018-06-21
## 5 2020-12-12 10.21105/joss.02727 2024-06-10 2475-9066 56 2020-12-12
## 6 2018-08-20 10.21105/joss.00850 2024-02-09 2475-9066 28 2018-08-20
## member page prefix publisher score source reference.count
## 1 8722 3598 10.21105 The Open Journal 0 Crossref 8
## 2 8722 2752 10.21105 The Open Journal 0 Crossref 13
## 3 8722 3917 10.21105 The Open Journal 0 Crossref 34
## 4 8722 656 10.21105 The Open Journal 0 Crossref 7
## 5 8722 2727 10.21105 The Open Journal 0 Crossref 33
## 6 8722 850 10.21105 The Open Journal 0 Crossref 7
## references.count is.referenced.by.count
## 1 8 10
## 2 13 1
## 3 34 0
## 4 7 44
## 5 33 8
## 6 7 17
## title
## 1 bfit: A Python Application For Beta-Detected NMR
## 2 The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes
## 3 CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX
## 4 QUIT: QUantitative Imaging Tools
## 5 PyQMRI: An accelerated Python based Quantitative MRI toolbox
## 6 powerbox: A Python package for creating structured fields with isotropic power spectra
## type url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.03598 6
## 2 journal-article http://dx.doi.org/10.21105/joss.02752 6
## 3 journal-article http://dx.doi.org/10.21105/joss.03917 6
## 4 journal-article http://dx.doi.org/10.21105/joss.00656 3
## 5 journal-article http://dx.doi.org/10.21105/joss.02727 5
## 6 journal-article http://dx.doi.org/10.21105/joss.00850 3
## short.container.title
## 1 JOSS
## 2 JOSS
## 3 JOSS
## 4 JOSS
## 5 JOSS
## 6 JOSS
## author
## 1 http://orcid.org/0000-0003-2847-2053, FALSE, Derek, Fujimoto, first
## 2 Vincent, Maximilien, Felix, Manuel, Jean-Claude, Simon, Dieter, Berenz, Naveau, Widmaier, Wüthrich, Passy, Guist, Büchler, first, additional, additional, additional, additional, additional, additional
## 3 http://orcid.org/0000-0003-2217-4768, FALSE, Shailesh, Kumar, first
## 4 http://orcid.org/0000-0001-7640-5520, FALSE, Tobias, C Wood, first
## 5 http://orcid.org/0000-0002-7800-0022, NA, http://orcid.org/0000-0001-6018-7821, http://orcid.org/0000-0002-4969-3878, FALSE, NA, FALSE, FALSE, Oliver, Stefan, Markus, Rudolf, Maier, Spann, Bödenler, Stollberger, first, additional, additional, additional
## 6 http://orcid.org/0000-0003-3059-3823, FALSE, Steven, G. Murray, first
## citation_count openalex_id affil_countries_all
## 1 11 https://openalex.org/W3203815452 CA
## 2 1 https://openalex.org/W3204758414 DE
## 3 0 https://openalex.org/W4200596790 IN
## 4 50 https://openalex.org/W2808854165 GB
## 5 8 https://openalex.org/W3110886256 AT
## 6 18 https://openalex.org/W3098853822 AU
## affil_countries_first
## 1 CA
## 2 DE
## 3 IN
## 4 GB
## 5 AT
## 6 AU
## api_title
## 1 bfit: A Python Application For Beta-Detected NMR
## 2 The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes
## 3 CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX
## 4 QUIT: QUantitative Imaging Tools
## 5 PyQMRI: An accelerated Python based Quantitative MRI toolbox
## 6 powerbox: A Python package for creating structured fields with isotropic power spectra
## api_state editor reviewers
## 1 accepted @lucydot @nicksisco1932,@jpata
## 2 accepted @gkthiruvathukal @traversaro,@vissarion
## 3 accepted @pdebuyl @Saran-nns,@mirca
## 4 accepted @cMadan @oesteban
## 5 accepted @Kevin-Mattheus-Moerman @grlee77,@agahkarakuzu,@DARSakthi
## 6 accepted @arfon @dfm
## nbr_reviewers repo_url
## 1 2 https://github.com/dfujim/bfit
## 2 2 https://github.com/intelligent-soft-robots/o80
## 3 2 https://github.com/carnotresearch/cr-sparse
## 4 1 https://github.com/spinicist/QUIT
## 5 3 https://github.com/IMTtugraz/PyQMRI/tree/JOSS_pub
## 6 1 https://github.com/steven-murray/powerbox
## review_issue_id prereview_issue_id languages
## 1 3598 3405 Python,C,C++
## 2 2752 2459 C++
## 3 3917 3913 Python
## 4 656 652 Python,C++
## 5 2727 2718 Python,C
## 6 850 842 Jupyter Notebook,Python
## archive_doi
## 1 https://doi.org/10.5281/zenodo.5519795
## 2 https://doi.org/10.5281/zenodo.5357876
## 3 https://doi.org/10.5281/zenodo.5749792
## 4 https://doi.org/10.5281/zenodo.1292086
## 5 https://doi.org/10.5281/zenodo.4313301
## 6 https://doi.org/10.5281/zenodo.1400822
## review_title
## 1 bfit: A Python Application For Beta-Detected NMR
## 2 The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes
## 3 CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX
## 4 Quantitative Imaging Tools
## 5 PyQMRI: An accelerated Python based Quantitative MRI Python toolbox
## 6 powerbox: A Python package for creating structured fields with isotropic power spectra
## review_number review_state review_opened review_closed review_ncomments
## 1 3598 closed 2021-08-10 2021-09-30 42
## 2 2752 closed 2020-10-15 2021-10-05 106
## 3 3917 closed 2021-11-16 2021-12-02 59
## 4 656 closed 2018-03-28 2018-06-21 23
## 5 2727 closed 2020-10-07 2020-12-12 90
## 6 850 closed 2018-07-26 2018-08-21 22
## review_labels
## 1 accepted,Python,C++,C,recommend-accept,published
## 2 accepted,Shell,C++,CMake,recommend-accept,published
## 3 accepted,TeX,Shell,Python,recommend-accept,published
## 4 accepted,recommend-accept,published
## 5 accepted,Shell,Python,C,recommend-accept,published
## 6 accepted,recommend-accept,published
## prerev_title
## 1 bfit: A Python Application For Beta-Detected NMR
## 2 The o80 C++ templated toolbox: Designing customized Python APIs for synchronizing realtime processes
## 3 CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX
## 4 Quantitative Imaging Tools
## 5 PyQMRI: An accelerated Python based Quantitative MRI Python toolbox
## 6 powerbox: A Python package for creating structured fields with isotropic power spectra
## prerev_state prerev_opened prerev_closed prerev_ncomments
## 1 closed 2021-06-25 2021-08-10 46
## 2 closed 2020-07-09 2020-10-15 44
## 3 closed 2021-11-12 2021-11-16 29
## 4 closed 2018-03-27 2018-03-28 44
## 5 closed 2020-10-02 2020-10-07 39
## 6 closed 2018-07-24 2018-07-26 21
## prerev_labels days_in_pre days_in_rev to_review repo_created
## 1 Python,C++,C 46 days 51 days TRUE 2018-11-30
## 2 Shell,C++,CMake 98 days 355 days TRUE 2020-03-31
## 3 TeX,Shell,Python 4 days 16 days TRUE 2020-12-22
## 4 TeX,Shell,CMake 1 days 85 days TRUE 2015-06-08
## 5 Shell,Python,C 5 days 66 days TRUE 2018-10-24
## 6 Python,Jupyter Notebook 2 days 26 days TRUE 2016-10-27
## repo_updated repo_pushed repo_nbr_stars repo_language
## 1 2024-02-06 2024-02-15 0 Python
## 2 2024-07-08 2024-07-08 8 C++
## 3 2024-06-17 2023-10-17 86 Jupyter Notebook
## 4 2024-07-04 2024-05-24 58 C++
## 5 2024-07-07 2024-04-04 29 Python
## 6 2024-06-27 2024-06-27 24 Jupyter Notebook
## repo_languages_bytes
## 1 Python:742843,C:15552,TeX:12041,C++:9272,Cython:4543,Meson:4398
## 2 C++:194642,CMake:4215,TeX:3739
## 3 Jupyter Notebook:1232323,Python:632797,TeX:18209,Shell:187
## 4 C++:662653,Python:131266,CMake:6191,C:3426,TeX:2770,Shell:1024
## 5 Python:1242118,C:288247,Dockerfile:1213,Shell:163
## 6 Jupyter Notebook:624459,Python:80215,TeX:23791,Makefile:2101
## repo_topics
## 1 b-nmr,b-nqr,triumf
## 2
## 3 sparse-representations,jax,wavelets,convex-optimization,linear-operators,compressive-sensing,functional-programming,l1-regularization,sparse-linear-systems,lasso,sparse-bayesian-learning,basis-pursuit
## 4
## 5
## 6
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 gpl-3.0 2 2 2024-07-11
## 2 bsd-3-clause 4 3 2024-07-11
## 3 apache-2.0 2 1 2024-07-11
## 4 mpl-2.0 8 3 2024-07-11
## 5 apache-2.0 4 4 2024-07-11
## 6 other 5 4 2024-07-11
## published.date halfyear nbr_authors
## 1 2021-09-30 2021H2 1
## 2 2021-10-05 2021H2 7
## 3 2021-12-02 2021H2 1
## 4 2018-06-21 2018H1 1
## 5 2020-12-12 2020H2 4
## 6 2018-08-20 2018H2 1
To read the current version of this file directly from GitHub, use the following code:
## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] openalexR_1.4.0 stringr_1.5.1 gt_0.11.0 rworldmap_1.3-8
## [5] sp_2.1-4 readr_2.1.5 citecorp_0.3.0 plotly_4.10.4
## [9] DT_0.33 jsonlite_1.8.8 purrr_1.0.2 gh_1.4.1
## [13] lubridate_1.9.3 ggplot2_3.5.1 tidyr_1.3.1 dplyr_1.1.4
## [17] rcrossref_1.2.009 tibble_3.2.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2 viridis_0.6.5
## [5] urltools_1.7.3 fields_16.2 fastmap_1.2.0 lazyeval_0.2.2
## [9] promises_1.3.0 digest_0.6.36 dotCall64_1.1-1 timechange_0.3.0
## [13] mime_0.12 lifecycle_1.0.4 terra_1.7-78 magrittr_2.0.3
## [17] compiler_4.4.1 rlang_1.1.4 sass_0.4.9 tools_4.4.1
## [21] wordcloud_2.6 utf8_1.2.4 yaml_2.3.9 data.table_1.15.4
## [25] knitr_1.48 labeling_0.4.3 fauxpas_0.5.2 htmlwidgets_1.6.4
## [29] bit_4.0.5 curl_5.2.1 RColorBrewer_1.1-3 plyr_1.8.9
## [33] xml2_1.3.6 httpcode_0.3.0 miniUI_0.1.1.1 withr_3.0.0
## [37] triebeard_0.4.1 grid_4.4.1 fansi_1.0.6 xtable_1.8-4
## [41] colorspace_2.1-0 gitcreds_0.1.2 scales_1.3.0 crul_1.4.2
## [45] cli_3.6.3 rmarkdown_2.27 crayon_1.5.3 generics_0.1.3
## [49] httr_1.4.7 tzdb_0.4.0 cachem_1.1.0 splines_4.4.1
## [53] maps_3.4.2 parallel_4.4.1 vctrs_0.6.5 Matrix_1.7-0
## [57] hms_1.1.3 bit64_4.0.5 crosstalk_1.2.1 jquerylib_0.1.4
## [61] glue_1.7.0 spam_2.10-0 codetools_0.2-20 stringi_1.8.4
## [65] gtable_0.3.5 later_1.3.2 raster_3.6-26 munsell_0.5.1
## [69] pillar_1.9.0 rappdirs_0.3.3 htmltools_0.5.8.1 R6_2.5.1
## [73] httr2_1.0.1 vroom_1.6.5 evaluate_0.24.0 shiny_1.8.1.1
## [77] lattice_0.22-6 highr_0.11 httpuv_1.6.15 bslib_0.7.0
## [81] Rcpp_1.0.12 gridExtra_2.3 nlme_3.1-164 mgcv_1.9-1
## [85] whisker_0.4.1 xfun_0.45 pkgconfig_2.0.3